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Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

EJ

26 мар. 2018 г.

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

QP

24 июня 2018 г.

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

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автор: John H

•22 авг. 2019 г.

This have been a very good and comprehensive introduction to Machine Learning, IMHO. It have given me the all basic introduction to ML that I could have hoped for. (I'm a senior practitioner of many forms of mathematical modelling and programming, as a former Astrophysics Phd.)

In particular, Andrew Ng is an excellent and experienced lecturer, and it's something that shows in that the course have been tested on thousands of students and over long time, such that for example exercises work very well in every little detail. (Sometimes quizzes may seem a little picky having to get nearly every little question right - but it's for really getting the understanding solid, and you can always improve your grade.)

Therefore, this must be a very good choice as an ML introduction, provided that you're willing to put in the effort of a few weeks on full time. (Albeit 11 weeks is for 'normal' university study schedule, and the course can be completed much faster on full time.) It should also compare well in generality compared to other courses (like Googles Machine Learning Crash Course).

автор: Mark M

•11 авг. 2016 г.

Professor Ng is a great teacher, his course is both challenging and satisfying. The exercises require you to take one step beyond the lecture -- not just parrot back the transcript -- you have to think about the implications of what you've just studied. Yet Ng's presentations are lucid and informative and that next step is obvious, once you think about it.

My greatest challenge is that, although I have been programming for decades, I've only dabbled in a functional language like Octave and my last math class dates back to the 70s. However, the math requirements are not onerous and I'm struggling through the Octave assignments with some success.

Although the course is 11 weeks there are more than 16 lectures as some weeks have two complete sets of lectures PLUS there are assignments every week that take a few hours to complete. So while there is a little more work in this course than in other Coursera offerings there is great value for the money and time spent.

If you're interested in Machine Learning this course is a great place to start.

автор: Tanmoy S

•17 февр. 2021 г.

No matter how much I appreciate this course it's never enough.

As an absolute beginner in ML, I never found a course / source that would explain why and how things work the way they work until I stumbled upon this one through recommendation.

Given most of the courses are in Python / R they fail to explain in - depth workings of the algorithms as everything is readymade there but this one nailed it!

The assignments are seriously wonderful and gives an excellent real - world view on where and how a particular technique works.

So, if you are hesitating because of the course being done in MATLAB / Octave, I would say that go for this course, first learn the workings and then you'll be able to implement them in Python in no time :)

Again, a massive amount of respect and thanks to Andrew Ng and everyone involved in the making of this course along with the superb community.

Final word: You'll love this one if you're a beginner and want to get a kickstart in ML. Thank you & have a great day :)

автор: Yintao L

•20 июня 2020 г.

This course is an intro-level course for Machine Learning which mainly focus on the implementation of those algorithms. It doesn't mention much math behind which makes it suitable for people even have no previous knowledge in related area. But make sure you have at least basic knowledge about linear algebra and calculus. You won't need those for the exercises but would help you better understand the course.

The exercises are really helpful for students to understand the material. If you want to learn more, I deeply suggest you not only finish the required exercises but also the extra exercises for each week.

Besides that, Professor Andrew's explanation and illustration is really clear and easy to understand. Even though this course has been online for many years, it contains the knowledge that still practical nowadays.

Overall, Five star course and I strongly encourage people with little or no background knowledge but aiming to learn about machine learning start with this course.

автор: Ozgur U

•6 янв. 2020 г.

This is the first course I ever took on Machine Learning. I have a good background in linear algebra. Therefore, Mathematical aspects of the course was not a big challenge for me. At the same time, Professor Ng explains the ideas behind each ML algorithm in an easily comprehensible manner. It is easy to follow his videos except the sound quality. I would strongly recommend that they improve sound quality.

The quizzes are not very challenging and easily doable if you understand the lectures.

The assignments are easier than I expected. The whole structure of the algorithm is given to you and some parts of the assignments simply require writing one or two lines of codes. I would recommend them adding a capstone project at the end of the lectures so we can apply what we learned.

Overall, if you are looking for a fundamental introduction to ML and posses a basic knowledge in college level linear algebra, I would strongly recommend this course to you.

автор: Vikrant K 4 B C S & E I V

•30 авг. 2019 г.

It's so wonderful that it can't be explained by the words and at the same time i am very sad that Ng sir has left us . i just love Ng sir , He is so wonderful person and teacher that can't be explained by the words .It's quite bit a big dream but i am dreaming of some day in the future where i am working with Ng sir on some machine learning problem and he is guiding me as he is doing now . I just love the course and also the mentors Mr. Neil Ostrove and Mr. Tom he had helped us to complete this course and assignment and also solved my useless something baby problems more carefully and i will help other student as guided by Ng sir in completing this course smoothely . and that's all . at the last i want to tell I just fall in love with Ng sir and coursera and the team . i have a big dream of meeting that my favourite Ng sir on some day.

Thank you

автор: Luca W

•19 янв. 2017 г.

Thank you Professor Ng for taking the time to produce such a phenomenal course. As mystifying as machine learning can appear to be, your well-paced and digestible teaching style gave me the opportunity to understand. With fantastic lectures, mid-video quizzes, end of topic quizzes, and programming assignments, you as a student are given all the resources you need to absorb the material.

These eleven weeks really gave me the perspective and knowledge I sought for. This is the first online course that I have taken and I am inspired and excited for the future of machine learning and e-learning. The final heartfelt video was a perfect conclusion and I wish to return the sentiment of gratitude and appreciation.

Thank you again, and rest assured that your teaching is having a profound impact on peoples lives across the world.

автор: Tobias T

•5 июня 2019 г.

I've tried DataCamp and recently take my first course in Coursera. The difference is huge and important if anyone wish to learn more about ML or DS. This course does not focus much on 'just coding' the answer. It aims to teach you the logic, basic maths behind ML algorithms.

The coding exercise is challenging and fun aswell. It doesn't give you any 'fill in the blanks', so basically, after each exercise, you properly have some good understanding about the logic. Using Matlab/Octive is much better than I expect. Not that it is easy to use/understand, but it let you understand the Math better. e.g. when to transpose, how to use look at dimension before writing any codes. These exercises are at a level which you can easily transcend your understanding and knowledge to whatever Python or R you are using. !

автор: Lubin Q

•16 авг. 2020 г.

As a non-CS student, I really have learned a lot from this course, which does not only cover several typical algorithms, but also a lot of important concepts in ML. It can be told from all these lecture videos that Prof. Ng has put a great effort in this course - he is not just reading the pre-prepared materials; instead he has sincerely shared a lot of his experiences in industry and pointed out the typical pitfalls that a lot of ML engineers have fallen in. This really inspires me and lets me develop a lot of awareness to avoid similar mistakes in the future.

Although this course is not the end of ML study, it is an excellent course as an introduction of ML for beginners to start with. Thanks Prof. Ng and your mentor team for all your efforts.

автор: Christian D

•18 авг. 2020 г.

Excellent introduction class to ML! Prof Ng provides clear explanations always and makes Machine Learning simple. I have learned to go with the flow of the videos, not worrying when I was not understanding some parts knowing that a clear explanation would be provided in the following minutes. Although this course is not interactive, Prof. Ng communicates well with his passion, and always "responds" to my questions in the videos. The quiz and exercises are very well thought of, really testing that we learn the essential and got a good feeling for the concepts.

Thanks to Prof. Ng for this excellent class.

(note: I would be interested in a follow-up class on Machine Learning, is there another class from Prof Ng avaialble soon on Coursera?)

автор: RENZZO S

•29 окт. 2020 г.

Excellent course for a depp introduction to machine learning. The professor Andrew NG has a special way to explain complicated themes in a very simple and understandable way. In the main videos of this course is more intuition than deep math and statistical demonstrations, but if you eager to understand issues more deeply like me you will find in the "resources" area of the course links to the documentation and the lecture videos of the machine learning course given in Stanford, there you could find the math and statistical demonstrations, also a bunch more algorithms to learn. Also you will find links to refresh your calculus, linear algebra and statistical skills if needed and links to data repositories to practice your new skills.

автор: Arpit J S

•1 мая 2020 г.

Mr. Andrew Ng has mastery on Machine Learning. His method of teching is precise and lucid, often engaging us to think more on untouched aspects of ML. This was my first course and first step (a baby step) on any platform to understand and learn ML . Lucky to have enrolled for this amazing course and I sincerely thank him for being instructor on this subject and also tons of thanks to mentors who clear doubts in discussion forums. It helped a lot. Lastly , I think this course has clearly set my path towards advanced studies in ML. Although, statistics and some of the terms did bounce off my head few times, I hope to revisit and work on them more in future. Thankyou Andrew Ng Sir ! I am your fan now !!! :)

автор: amirhosein b

•3 июня 2020 г.

I so appreciate it from COURSERA and DR ANDREW NG for this unbelievable course. It was definitely one of the best courses I've ever seen in my whole 20-year life. I'm from Iran and I have really restricted rules for having access to such courses. I'm so glad to have this opportunity to attend a class with a professor from Stanford University. I'm not good at English very well but I don't know why I feel that at the end of the class Prof NG was kind of sad from ending the course and I was nearly to cry seeing him like this. here I'm gonna promise this for the first time, I promise to spend my whole life to do what Prof NG did for me in this course, to help others. Thank you so very much.

автор: Vincent C

•25 сент. 2019 г.

After finishing the course, I feel much more confident in pursuing more advanced machine learning. The course teaches everything intuitively and in detail but maybe it could use some improvement to achieve perfection. It would be better if the course could provide pointers to some of the topics beyond the scope of the course such as the derivation of the back propagation, svm, pca, etc. Because often times when you search for derivations they might not be very useful for your levels, if course could provide some good references as some lecture notes after the video would be great for the students to gain even more solid groundings of the things behind the hood

Super thanks and thumbs up

автор: Vamshi B

•6 июня 2019 г.

As a machine learning newbie, I can say this course is really helpful to get in depth intuition on how machine learning algorithms work. Techniques to evaluate and improve our algorithms are also explained very well. Programming exercises are really challenging. Review questions are also crafted well. Though this course uses Octave/Matlab instead of python for programming, I find it quite useful to understand and implement algorithms easily. Only negative of this course is, mathematics involved is not explained in detail. Overall, this course has helped me a lot to understand machine learning in a better and useful way.

автор: DEEPANJYOTI S

•11 мар. 2019 г.

This is a very good course which gives a good solid foundation in the basics concepts of Machine Learning. Prof. Andrew explains reasonably complicated algorithms in a very intuitive way which goes reasonably deep, but at the same time doesn't overwhelm the student with a lot of underlying mathematics. The course structure also follows a very natural progression (linear regression --> logistic regression --> neural network --> SVM) and bringing in other basic concepts like feature normalization, regularization, measurements etc. along the way. Definitely one of the better designed courses I've seen so far.

автор: Tun C

•2 февр. 2018 г.

I've been working with machine learning for a while and I've used different supervised and unsupervised algorithms. However, this course taught me about how these different machine learning algorithms work under the hood. Professor Ng is a great teacher. His method of describing the problem set, giving the intuition on how to go about solving the problem and slowly defining the algorithm works very well. This course has the right amount of breadth by covering only the most applicable algorithms and has the right amount of depth by covering the math and the intuition behind each algorithm.

автор: Maria V

•6 дек. 2020 г.

This is the most amazing class that I have taken in a long time. The attention to detail is incredible. I appreciated the most all the context Andrew gives around evaluating algorithms and models, reasoning about finding errors and taking steps to improve the performance. This course gives you so much more than just the algorithms and makes sure you think for yourself and truly understand the topics.

One thing that I would suggest as an improvement is video editing, since sometimes sentences are repeated in a way that indicates that the previous sentence should have been edited out.

автор: Anith S

•6 июня 2019 г.

This is the first ever course I have taken on Machine Learning and I have to say that it was the best course that I have ever taken till I have taken the DeepLearinig Specialization by Andrew Ng.

I would highly recommend this course for anyone who wants to break into Machine Learning. Because it starts with the very basics and builds on it.

It currently may be bit outdated considering that it is thought using Matlab and not Python but it is excellent in explaining the core concepts and the algorithms of Machine Learning.

It is still a good course for breaking into Machine Learning.

автор: Zheng Y

•23 февр. 2019 г.

The course is very well structured for me, a student who has some understanding of machine learning but would like to get a systematic introduction of the subject.

The course strikes a balance between depth and breadth. The amount of math and equations are just right. Prof. Ng did a good job stimulating the students' curiosity to dive deeper. And for those who want to get practical and hands-on, this course contains enough tools for machine learning practitioners.

I would recommend this course to anyone who is interested in machine learning but do not know where to start.

автор: John W

•18 авг. 2020 г.

I would give this class 4.5 stars (rounds up to 5). Many different ML topics are covered, and they are presented at an appropriate pace for learning. The programming assignments are a great way to review the content and make sure you understand some of the details. Past experience with linear (matrix) algebra will be helpful but not required. Be sure to consult the resources that are available, especially the errata (it was a little disappointing how many small errors are present) and the lecture notes. But overall, I highly recommend this course.

автор: Walter E P

•23 дек. 2019 г.

Great Course!. I took this course after having been formally trained in topics such as Numerical Optimization, Neural Networks, Genetic Algorithms, Linear Regression and other topics and I found these classes to be both very informative and refreshing. Learned something that sometimes some courses out there forget to mention which is how to draw meaningful statistics to analyze your algorithms performance and also things like what do work on next. I definitely advice people to take this course even if you are a pretty advanced learner on these topics.

автор: Paweł M

•24 мар. 2021 г.

Fantastic course! I highly recommend it to anyone who wants to look a little more "under the hood" of ML. There are many courses that simply teach you how to use certain tools, such as Pandas or Tensor Flow, but often without explaining what the algorithm does or what kind of math operations are involved. This course shows it, but fear not - it's not as mathematically advanced as it could be - just enough to understand the topic. Professor Ng is a great teacher, I wish my professors at the time I studied were like him. Thank You Professor Ng!

автор: Vivek R

•12 мар. 2019 г.

This course is very well designed, covers a lot of topics with a lot of rigourous detail, but Andrew Ng introduces them giving some intuition about them, before diving into the deeper Maths. Assignments are very challenging, but with some boilerplate code already done, they are immensely satisfying, as you end up achieving with some implementations of pretty cool problems. I have done linear algebra and regression and PCA before, so was able to complete it rather quickly, but this should be very approachable and useful for everyone.

автор: Kohei K

•2 окт. 2020 г.

I am based in Tokyo, Japan and working for Marketing in Hewlett-Packard Enterprise. Marketing is now digital and data driven. In order to improve marketing data science skill, I took this course. This course and Professor Andrew Ng is amazing and could learn Machine Learning comprehensively. Recommendation system and clustering is very relevant to marketing job and would like contribute to the world based on the knowledge what I learned in this course. Many thanks for your guidance and great teaching, Professor Andrew san !

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